Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations712
Missing cells684
Missing cells (%)8.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory202.5 KiB
Average record size in memory291.2 B

Variable types

Numeric6
Categorical4
Text2

Alerts

Fare is highly overall correlated with familyMemHigh correlation
Parch is highly overall correlated with familyMemHigh correlation
Sex is highly overall correlated with SurvivedHigh correlation
SibSp is highly overall correlated with familyMemHigh correlation
Survived is highly overall correlated with SexHigh correlation
familyMem is highly overall correlated with Fare and 2 other fieldsHigh correlation
Age has 131 (18.4%) missing values Missing
Cabin has 551 (77.4%) missing values Missing
PassengerId is uniformly distributed Uniform
PassengerId has unique values Unique
SibSp has 488 (68.5%) zeros Zeros
Parch has 538 (75.6%) zeros Zeros
Fare has 9 (1.3%) zeros Zeros
familyMem has 428 (60.1%) zeros Zeros

Reproduction

Analysis started2025-07-13 16:21:43.628580
Analysis finished2025-07-13 16:21:48.586734
Duration4.96 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

PassengerId
Real number (ℝ)

Uniform  Unique 

Distinct712
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean445.56882
Minimum1
Maximum890
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2025-07-13T21:51:48.702574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile52.55
Q1220.75
median441.5
Q3666.25
95-th percentile841.45
Maximum890
Range889
Interquartile range (IQR)445.5

Descriptive statistics

Standard deviation257.00666
Coefficient of variation (CV)0.57680577
Kurtosis-1.2211051
Mean445.56882
Median Absolute Deviation (MAD)224
Skewness0.0040507375
Sum317245
Variance66052.426
MonotonicityNot monotonic
2025-07-13T21:51:48.817148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
248 1
 
0.1%
555 1
 
0.1%
414 1
 
0.1%
512 1
 
0.1%
670 1
 
0.1%
105 1
 
0.1%
718 1
 
0.1%
7 1
 
0.1%
608 1
 
0.1%
516 1
 
0.1%
Other values (702) 702
98.6%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
7 1
0.1%
10 1
0.1%
11 1
0.1%
12 1
0.1%
13 1
0.1%
14 1
0.1%
ValueCountFrequency (%)
890 1
0.1%
887 1
0.1%
886 1
0.1%
885 1
0.1%
882 1
0.1%
881 1
0.1%
880 1
0.1%
879 1
0.1%
877 1
0.1%
873 1
0.1%

Pclass
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size56.5 KiB
3
400 
1
169 
2
143 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters712
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 400
56.2%
1 169
23.7%
2 143
 
20.1%

Length

2025-07-13T21:51:48.941691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-13T21:51:49.036912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 400
56.2%
1 169
23.7%
2 143
 
20.1%

Most occurring characters

ValueCountFrequency (%)
3 400
56.2%
1 169
23.7%
2 143
 
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 712
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 400
56.2%
1 169
23.7%
2 143
 
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 712
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 400
56.2%
1 169
23.7%
2 143
 
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 712
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 400
56.2%
1 169
23.7%
2 143
 
20.1%

Sex
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size59.1 KiB
male
464 
female
248 

Length

Max length6
Median length4
Mean length4.6966292
Min length4

Characters and Unicode

Total characters3344
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfemale
2nd rowfemale
3rd rowmale
4th rowmale
5th rowmale

Common Values

ValueCountFrequency (%)
male 464
65.2%
female 248
34.8%

Length

2025-07-13T21:51:49.164642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-13T21:51:49.254106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
male 464
65.2%
female 248
34.8%

Most occurring characters

ValueCountFrequency (%)
e 960
28.7%
m 712
21.3%
a 712
21.3%
l 712
21.3%
f 248
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3344
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 960
28.7%
m 712
21.3%
a 712
21.3%
l 712
21.3%
f 248
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3344
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 960
28.7%
m 712
21.3%
a 712
21.3%
l 712
21.3%
f 248
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3344
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 960
28.7%
m 712
21.3%
a 712
21.3%
l 712
21.3%
f 248
 
7.4%

Age
Real number (ℝ)

Missing 

Distinct85
Distinct (%)14.6%
Missing131
Missing (%)18.4%
Infinite0
Infinite (%)0.0%
Mean29.234079
Minimum0.42
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2025-07-13T21:51:49.360912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile4
Q120
median28
Q337
95-th percentile56
Maximum80
Range79.58
Interquartile range (IQR)17

Descriptive statistics

Standard deviation14.345728
Coefficient of variation (CV)0.49071932
Kurtosis0.23012296
Mean29.234079
Median Absolute Deviation (MAD)8
Skewness0.42229824
Sum16985
Variance205.7999
MonotonicityNot monotonic
2025-07-13T21:51:49.496155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 24
 
3.4%
24 23
 
3.2%
19 20
 
2.8%
25 20
 
2.8%
28 20
 
2.8%
21 20
 
2.8%
30 18
 
2.5%
22 18
 
2.5%
36 17
 
2.4%
31 17
 
2.4%
Other values (75) 384
53.9%
(Missing) 131
 
18.4%
ValueCountFrequency (%)
0.42 1
 
0.1%
0.75 2
 
0.3%
0.83 2
 
0.3%
0.92 1
 
0.1%
1 5
0.7%
2 7
1.0%
3 5
0.7%
4 9
1.3%
5 4
0.6%
6 2
 
0.3%
ValueCountFrequency (%)
80 1
 
0.1%
74 1
 
0.1%
71 1
 
0.1%
70.5 1
 
0.1%
65 3
0.4%
64 2
0.3%
63 2
0.3%
62 4
0.6%
61 2
0.3%
60 2
0.3%

SibSp
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52668539
Minimum0
Maximum8
Zeros488
Zeros (%)68.5%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2025-07-13T21:51:49.662877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2.45
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1260207
Coefficient of variation (CV)2.137938
Kurtosis17.662706
Mean0.52668539
Median Absolute Deviation (MAD)0
Skewness3.7091356
Sum375
Variance1.2679226
MonotonicityNot monotonic
2025-07-13T21:51:49.765457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 488
68.5%
1 164
 
23.0%
2 24
 
3.4%
4 15
 
2.1%
3 10
 
1.4%
8 6
 
0.8%
5 5
 
0.7%
ValueCountFrequency (%)
0 488
68.5%
1 164
 
23.0%
2 24
 
3.4%
3 10
 
1.4%
4 15
 
2.1%
5 5
 
0.7%
8 6
 
0.8%
ValueCountFrequency (%)
8 6
 
0.8%
5 5
 
0.7%
4 15
 
2.1%
3 10
 
1.4%
2 24
 
3.4%
1 164
 
23.0%
0 488
68.5%

Parch
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.39606742
Minimum0
Maximum6
Zeros538
Zeros (%)75.6%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2025-07-13T21:51:49.870355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.83549003
Coefficient of variation (CV)2.1094642
Kurtosis10.090856
Mean0.39606742
Median Absolute Deviation (MAD)0
Skewness2.8054592
Sum282
Variance0.69804358
MonotonicityNot monotonic
2025-07-13T21:51:49.958997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 538
75.6%
1 96
 
13.5%
2 65
 
9.1%
5 5
 
0.7%
4 4
 
0.6%
3 3
 
0.4%
6 1
 
0.1%
ValueCountFrequency (%)
0 538
75.6%
1 96
 
13.5%
2 65
 
9.1%
3 3
 
0.4%
4 4
 
0.6%
5 5
 
0.7%
6 1
 
0.1%
ValueCountFrequency (%)
6 1
 
0.1%
5 5
 
0.7%
4 4
 
0.6%
3 3
 
0.4%
2 65
 
9.1%
1 96
 
13.5%
0 538
75.6%

Ticket
Text

Distinct565
Distinct (%)79.4%
Missing0
Missing (%)0.0%
Memory size60.4 KiB
2025-07-13T21:51:50.165063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length6.6488764
Min length3

Characters and Unicode

Total characters4734
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique469 ?
Unique (%)65.9%

Sample

1st row250649
2nd rowC.A. 29395
3rd row244367
4th row364512
5th row14973
ValueCountFrequency (%)
pc 42
 
4.7%
c.a 22
 
2.5%
a/5 14
 
1.6%
ca 13
 
1.5%
ston/o 9
 
1.0%
2 9
 
1.0%
soton/o.q 7
 
0.8%
1601 7
 
0.8%
soton/oq 6
 
0.7%
sc/paris 6
 
0.7%
Other values (586) 752
84.8%
2025-07-13T21:51:50.524308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 592
12.5%
1 567
12.0%
2 468
9.9%
7 383
8.1%
4 374
7.9%
6 339
 
7.2%
0 323
 
6.8%
5 302
 
6.4%
9 266
 
5.6%
8 229
 
4.8%
Other values (22) 891
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4734
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 592
12.5%
1 567
12.0%
2 468
9.9%
7 383
8.1%
4 374
7.9%
6 339
 
7.2%
0 323
 
6.8%
5 302
 
6.4%
9 266
 
5.6%
8 229
 
4.8%
Other values (22) 891
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4734
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 592
12.5%
1 567
12.0%
2 468
9.9%
7 383
8.1%
4 374
7.9%
6 339
 
7.2%
0 323
 
6.8%
5 302
 
6.4%
9 266
 
5.6%
8 229
 
4.8%
Other values (22) 891
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4734
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 592
12.5%
1 567
12.0%
2 468
9.9%
7 383
8.1%
4 374
7.9%
6 339
 
7.2%
0 323
 
6.8%
5 302
 
6.4%
9 266
 
5.6%
8 229
 
4.8%
Other values (22) 891
18.8%

Fare
Real number (ℝ)

High correlation  Zeros 

Distinct223
Distinct (%)31.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.526451
Minimum0
Maximum512.3292
Zeros9
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2025-07-13T21:51:50.656188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.225
Q17.8958
median14.45625
Q331
95-th percentile110.8833
Maximum512.3292
Range512.3292
Interquartile range (IQR)23.1042

Descriptive statistics

Standard deviation51.439367
Coefficient of variation (CV)1.5814627
Kurtosis35.08937
Mean32.526451
Median Absolute Deviation (MAD)6.86665
Skewness4.9748814
Sum23158.833
Variance2646.0085
MonotonicityNot monotonic
2025-07-13T21:51:50.778084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 37
 
5.2%
7.8958 32
 
4.5%
8.05 30
 
4.2%
7.75 28
 
3.9%
26 25
 
3.5%
10.5 17
 
2.4%
7.925 14
 
2.0%
7.2292 13
 
1.8%
7.8542 13
 
1.8%
26.55 13
 
1.8%
Other values (213) 490
68.8%
ValueCountFrequency (%)
0 9
1.3%
4.0125 1
 
0.1%
5 1
 
0.1%
6.2375 1
 
0.1%
6.4375 1
 
0.1%
6.45 1
 
0.1%
6.4958 2
 
0.3%
6.75 2
 
0.3%
6.8583 1
 
0.1%
6.95 1
 
0.1%
ValueCountFrequency (%)
512.3292 3
0.4%
263 4
0.6%
262.375 1
 
0.1%
247.5208 2
0.3%
227.525 2
0.3%
221.7792 1
 
0.1%
211.5 1
 
0.1%
211.3375 2
0.3%
164.8667 2
0.3%
153.4625 2
0.3%

Cabin
Text

Missing 

Distinct125
Distinct (%)77.6%
Missing551
Missing (%)77.4%
Memory size47.2 KiB
2025-07-13T21:51:51.069764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length15
Median length3
Mean length3.6149068
Min length1

Characters and Unicode

Total characters582
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)59.6%

Sample

1st rowF33
2nd rowC93
3rd rowC118
4th rowE44
5th rowD33
ValueCountFrequency (%)
c23 4
 
2.1%
c27 4
 
2.1%
c25 4
 
2.1%
f 4
 
2.1%
c22 3
 
1.6%
c26 3
 
1.6%
f33 3
 
1.6%
b96 3
 
1.6%
b98 3
 
1.6%
f2 3
 
1.6%
Other values (128) 156
82.1%
2025-07-13T21:51:51.497793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 63
10.8%
2 53
 
9.1%
3 51
 
8.8%
B 47
 
8.1%
1 43
 
7.4%
6 40
 
6.9%
5 38
 
6.5%
7 31
 
5.3%
8 30
 
5.2%
29
 
5.0%
Other values (9) 157
27.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 582
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 63
10.8%
2 53
 
9.1%
3 51
 
8.8%
B 47
 
8.1%
1 43
 
7.4%
6 40
 
6.9%
5 38
 
6.5%
7 31
 
5.3%
8 30
 
5.2%
29
 
5.0%
Other values (9) 157
27.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 582
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 63
10.8%
2 53
 
9.1%
3 51
 
8.8%
B 47
 
8.1%
1 43
 
7.4%
6 40
 
6.9%
5 38
 
6.5%
7 31
 
5.3%
8 30
 
5.2%
29
 
5.0%
Other values (9) 157
27.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 582
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 63
10.8%
2 53
 
9.1%
3 51
 
8.8%
B 47
 
8.1%
1 43
 
7.4%
6 40
 
6.9%
5 38
 
6.5%
7 31
 
5.3%
8 30
 
5.2%
29
 
5.0%
Other values (9) 157
27.0%

Embarked
Categorical

Distinct3
Distinct (%)0.4%
Missing2
Missing (%)0.3%
Memory size56.5 KiB
S
513 
C
139 
Q
58 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters710
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 513
72.1%
C 139
 
19.5%
Q 58
 
8.1%
(Missing) 2
 
0.3%

Length

2025-07-13T21:51:51.649050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-13T21:51:51.772150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
s 513
72.3%
c 139
 
19.6%
q 58
 
8.2%

Most occurring characters

ValueCountFrequency (%)
S 513
72.3%
C 139
 
19.6%
Q 58
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 710
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 513
72.3%
C 139
 
19.6%
Q 58
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 710
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 513
72.3%
C 139
 
19.6%
Q 58
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 710
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 513
72.3%
C 139
 
19.6%
Q 58
 
8.2%

Survived
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size56.5 KiB
0
439 
1
273 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters712
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 439
61.7%
1 273
38.3%

Length

2025-07-13T21:51:51.888270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-13T21:51:51.987548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 439
61.7%
1 273
38.3%

Most occurring characters

ValueCountFrequency (%)
0 439
61.7%
1 273
38.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 712
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 439
61.7%
1 273
38.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 712
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 439
61.7%
1 273
38.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 712
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 439
61.7%
1 273
38.3%

familyMem
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.92275281
Minimum0
Maximum10
Zeros428
Zeros (%)60.1%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2025-07-13T21:51:52.085571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.658949
Coefficient of variation (CV)1.797826
Kurtosis8.8169438
Mean0.92275281
Median Absolute Deviation (MAD)0
Skewness2.7181653
Sum657
Variance2.7521117
MonotonicityNot monotonic
2025-07-13T21:51:52.199841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 428
60.1%
1 130
 
18.3%
2 82
 
11.5%
3 20
 
2.8%
5 19
 
2.7%
6 11
 
1.5%
4 10
 
1.4%
10 6
 
0.8%
7 6
 
0.8%
ValueCountFrequency (%)
0 428
60.1%
1 130
 
18.3%
2 82
 
11.5%
3 20
 
2.8%
4 10
 
1.4%
5 19
 
2.7%
6 11
 
1.5%
7 6
 
0.8%
10 6
 
0.8%
ValueCountFrequency (%)
10 6
 
0.8%
7 6
 
0.8%
6 11
 
1.5%
5 19
 
2.7%
4 10
 
1.4%
3 20
 
2.8%
2 82
 
11.5%
1 130
 
18.3%
0 428
60.1%

Interactions

2025-07-13T21:51:47.553287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:44.004344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:44.737200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:45.397326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:46.074502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:46.709031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:47.651459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:44.123380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:44.839317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:45.511514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:46.160764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:46.806158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:47.765544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:44.258649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:44.949333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:45.629053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:46.286765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:46.909544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:47.863602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:44.416778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:45.049619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:45.754113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:46.409429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:47.014887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:47.958925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:44.547543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:45.168608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:45.864981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:46.516579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:47.351505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:48.061264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:44.642309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:45.274642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:45.973529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:46.616328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-07-13T21:51:47.456081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-07-13T21:51:52.283304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AgeEmbarkedFareParchPassengerIdPclassSexSibSpSurvivedfamilyMem
Age1.0000.1160.115-0.2590.0320.2600.093-0.2000.151-0.241
Embarked0.1161.0000.1900.0590.0000.2580.0980.0670.1670.079
Fare0.1150.1901.0000.407-0.0420.4700.1870.4410.2770.527
Parch-0.2590.0590.4071.000-0.0220.0000.2540.4520.1580.808
PassengerId0.0320.000-0.042-0.0221.0000.0470.092-0.0820.095-0.078
Pclass0.2600.2580.4700.0000.0471.0000.1390.1390.3360.136
Sex0.0930.0980.1870.2540.0920.1391.0000.2240.5410.220
SibSp-0.2000.0670.4410.452-0.0820.1390.2241.0000.1750.844
Survived0.1510.1670.2770.1580.0950.3360.5410.1751.0000.197
familyMem-0.2410.0790.5270.808-0.0780.1360.2200.8440.1971.000

Missing values

2025-07-13T21:51:48.226046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-13T21:51:48.400671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-13T21:51:48.530455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PassengerIdPclassSexAgeSibSpParchTicketFareCabinEmbarkedSurvivedfamilyMem
2472482female24.00225064914.5000NaNS12
66672female29.000C.A. 2939510.5000F33S10
991002male34.01024436726.0000NaNS01
6146153male35.0003645128.0500NaNS00
5885893male22.000149738.0500NaNS00
2162173female27.000STON/O2. 31012837.9250NaNS10
3263273male61.0003453646.2375NaNS00
2242251male38.0101994390.0000C93S11
4314323femaleNaN1037656416.1000NaNS11
4714723male38.0003150898.6625NaNS00
PassengerIdPclassSexAgeSibSpParchTicketFareCabinEmbarkedSurvivedfamilyMem
6586592male23.0002975113.000NaNS00
3333343male16.02034576418.000NaNS02
1661671femaleNaN0111350555.000E33S11
8458463male42.000C.A. 55477.550NaNS00
2142153maleNaN103672297.750NaNQ01
5905913male35.000STON/O 2. 31012737.125NaNS00
4754761maleNaN0011046552.000A14S00
90913male29.0003432768.050NaNS00
1931942male3.01123008026.000F2S12
3003013femaleNaN0092347.750NaNQ10